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Introduction: Learning standards are a crucial determinant of computer science (CS) education at the K-12 level, but they are not often researched despite their importance. We sought to address this gap with a mixed-methods study examining state and national K-12 CS standards. Research Question: What are the similarities and differences between state and national computer science standards? Methods: We tagged the state CS standards (n = 9695) according to their grade band/level, topic, course, and similarity to a Computer Science Teachers Association (CSTA) standard. We also analyzed the content of standards similar to CSTA standards to determine their topics, cognitive complexity, and other features. Results: We found some commonalities amidst broader diversity in approaches to organization and content across the states, relative to the CSTA standards. The content analysis showed that a common difference between state and CSTA standards is that the state standards tend to include concrete examples. We also found differences across states in how similar their standards are to CSTA standards, as well as differences in how cognitively complex the standards are. Discussion: Standards writers face many tensions and trade-offs, and this analysis shows how – in general terms – various states have chosen to manage those trade-offs in writing standards. For example, adding examples can improve clarity and specificity, but perhaps at the cost of brevity and longevity. A better understanding of the landscape of state standards can assist future standards writers, curriculum developers, and researchers in their work.
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This content will become publicly available on February 18, 2026
What Can 10k State CS Standards Reveal about Learning? A New Dataset for Investigation
In the United States, state learning standards guide curriculum, assessment, teacher certification, and other key drivers of the student learning experience. Investigating standards allows us to answer a lot of big questions about the field of K-12 computer science (CS) education. Our team has created a dataset of state-level K-12 CS standards for all US states that currently have such standards (n = 42). This dataset was created by CS subject matter experts, who - for each of the approximately 10,000 state CS standards - manually tagged its assigned grade level/band, category/topic, and, if applicable, which CSTA standard it is identical or similar to. We also determined the standards' cognitive complexity using Bloom's Revised Taxonomy. Using the dataset, we were able to analyze each state's CS standards using a variety of metrics and approaches. To our knowledge, this is the first comprehensive, publicly available dataset of state CS standards that includes the factors mentioned previously. We believe that this dataset will be useful to other CS education researchers, including those who want to better understand the state and national landscape of K-12 CS education in the US, the characteristics of CS learning standards, the coverage of particular CS topics (e.g., cybersecurity, AI), and many other topics. In this lightning talk, we will introduce the dataset's features as well as some tools that we have developed (e.g., to determine a standard's Bloom's level) that may be useful to others who use the dataset.
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- Award ID(s):
- 2311746
- PAR ID:
- 10643582
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 1748 to 1748
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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